Abstract
Background: Given the worldwide trend of population aging, the importance of prolonging independence in older populations is critical. Nutrition has a significant influence on both physical and mental health and, if poor, it ultimately results in greater dependency on health care and support services. InterRAI-HC is a holistic geriatric needs assessment that includes contains a section on oral and nutritional status, and was recently adopted by the New Zealand government. InterRAI-HC has not been validated in a New Zealand setting and there is uncertainty around its ability to correctly classify whether an older adult is at nutritional risk. Therefore, this study aimed to compare the nutritional components of the oral and nutrition status domain of InterRAI-HC to that of two validated nutrition specific screening tools, SCREEN II and MNA-SF, and identify factors associated with nutritional risk among independent living older adults.
Methods: A convenience sample of 181 community dwelling older adults aged 65 years or older living in Dunedin city were recruited to participate in the study. A questionnaire incorporating the nutritional components of the oral and nutrition status domain of InterRAI-HC, SCREEN II and MNA-SF was designed, with additional socio-demographic and general health questions included. The questionnaire was piloted and revised before administering interviews in the participant’s own home to collect the questionnaire responses. The total score for SCREEN II and MNA-SF were calculated for each participant using the associated scoring systems. This enabled categorisation into the relative nutrition risk groups for each tool. For InterRAI-HC, the appropriate nutrition risk group was determined by their BMI and absence of terminal illness. Calculations of descriptive statistics were carried out using Microsoft Excel 2007 software. Further statistical analyses were performed using the statistical package STATA 11.1 (StataCorp LP. College Station, TX, USA). Univariate and multivariate regression analysis were used to identify variables associated with nutrition risk for SCREEN II, MNA-SF, and InterRAI-HC.
Results: Nutritional risk was present in 40.4% of participants using the SCREEN II criteria, with 23.8% ‘at risk’ (n=43), 16.6% ‘at high risk’ (n=30), and 59.7% with a normal nutrition status. A smaller percentage were identified to be at nutritional risk when using MNA-SF, with 11.6% (n=21) classified as ‘at risk of malnutrition, and 0.6% classified as ‘malnourished’ (n=1). However, InterRAI-HC detected the smallest proportion of participants at nutritional risk with only 3.3% (n=6) classed as ‘at risk of malnutrition’ and 1.1% (n=2) classed as ‘at high risk or malnutrition’. Marital status, living situation, presence of heart disease, self perceived health, and completion of meals were found to be inversely associated with the participant’s SCREEN II and MNA-SF scores.
Conclusions: Even in a generally healthy population of older adults, SCREEN II and MNA-SF categorised 12-40% or participants at nutritional risk, which was considerably higher than the <5% categorised at risk using InterRAI-HC. This suggests that the nutritional components of InterRAI-HC are less likely than validated nutrition screening tools to detect nutritional risk. Inclusion of some of the nutritional components of SCREEN II and MNA-SF to the oral and nutrition domain of interRAI-HC might be considered for future improvements.